This is my first Tidy Tuesday contribution and will be playing around a little bit with the the Coffee rating data .
Downloading file 1 of 1: `coffee_ratings.csv`
p1 <- coffee %>%
drop_na(any_of("country_of_origin")) %>%
filter(aroma != 0) %>%
ggplot() +
aes(x = total_cup_points, fill = country_of_origin) +
geom_density() +
theme_minimal() +
scale_fill_viridis_d(alpha = 0.7) +
ylab("Proportion of coffes per country") +
xlab("Total cup points") +
xlim(60,100) +
theme(
plot.title = element_text(size = 18, face = "bold"),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.y = element_text(size = 16),
legend.text = element_text(size = 14),
legend.title = element_blank(),
legend.position = "bottom"
)
p2 <- coffee %>%
drop_na(any_of("country_of_origin")) %>%
filter(aroma != 0) %>%
ggplot() +
aes(x = total_cup_points, y = country_of_origin, fill = country_of_origin) +
geom_boxplot(show.legend = FALSE) +
theme_minimal() +
scale_fill_viridis_d(alpha = 0.7) +
ylab("Countries") +
xlab("Total cup points") +
labs(fill = "Country") +
theme(
plot.title = element_text(size = 18, face = "bold"),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.y = element_text(size = 18)
) +
gghighlight(country_of_origin == "Colombia")
p2 / p1 + plot_annotation(tag_levels = 'A')A work by Camilo Garcia